# Lost Silence: An emergency response early detection service through continuous processing of telecommunication data streams

## TL;DR

This paper presents a real-time, semantic data stream processing method for early detection of traumatic events like ship capsizing using telecommunication data, enabling prompt emergency responses.

## Contribution

It introduces a novel approach that processes RDF-annotated telecommunication streams for immediate incident detection, demonstrated on a cruise ship capsizing scenario.

## Key findings

- Effective detection within milliseconds using semantic data streams
- Method is adaptable to various emergency scenarios
- Proper window size is crucial for detection efficiency

## Abstract

Early detection of significant traumatic events, e.g. a terrorist attack or a ship capsizing, is important to ensure that a prompt emergency response can occur. In the modern world telecommunication systems could play a key role in ensuring a successful emergency response by detecting such incidents through significant changes in calls and access to the networks. In this paper a methodology is illustrated to detect such incidents immediately (with the delay in the order of milliseconds), by processing semantically annotated streams of data in cellular telecommunication systems. In our methodology, live information about the position and status of phones are encoded as RDF streams. We propose an algorithm that processes streams of RDF annotated telecommunication data to detect abnormality. Our approach is exemplified in the context of a passenger cruise ship capsizing. However, the approach is readily translatable to other incidents. Our evaluation results show that with a properly chosen window size, such incidents can be detected efficiently and effectively.